Natural Language Processing: Revolutionizing the Venture Capital World
“How can I keep my customers happy?” This is the question that startups are constantly asking themselves. Whether it be through product design or through new initiatives, startups are created to serve the consumer. Natural Language Processing (NLP) has completely shifted the way that venture capital firms are able to invest in such revolutionizing startups and the way that startups are able to create and maintain customer loyalty.
NLP is a subfield of computer science, informational engineering, and artificial intelligence paired with the interactions between computers and human languages in order to better program computers to process and analyze large amounts of natural language data. NLP is used by computers to manipulate human language, whether that be to extract emotional meaning or to generate text. Despite being an active area of research for decades, NLP didn’t hit the venture-backed mainstream until fairly recently. Thanks to advances in computing hardware and (relatively) more user-friendly open source software frameworks coupled with the explosion of data, the last decade has been particularly propitious for both entrepreneurs and investors in the NLP sectors. Only after a set of enabling technologies emerged and a firm substrate of high-resolution data was laid down did entrepreneurs start founding companies and investors followed suit.
The following chart portrays the rapid growth from 2008 to 2017 in both deal and dollar volume for venture deals raised by companies in the AI, Machine Learning, and other technology related startups.
NLP has had and is continuing to have an enormous impact on both the venture capital side and the company side. One of the biggest challenges that venture capital firms face is finding interesting and promising investment targets before other competing firms do so. But, NLP and the subsequent productive analytics are starting to transform the process of portfolios for investors. By combining internet data with NLP, venture capital firms have discovered innovative ways to traverse through thousands of prospects in order to find the best potential investments. InReach Ventures co-founder Roberto Bonanzinga has invested $7 million on software that utilizes machine learning and NLP to find worthwhile European startups to invest in. The software determines the ideal investment based on the management team, product development, and website traffic. NLP has significantly contributed to the scalability of the investment process. What used to be a handcrafted job has now been extremely productive and efficient.
NLP, a technological breakthrough, has taken almost every business industry by storm. In particular, the customer service experience aspect for startups is gaining momentum due to the disruption of NLP. NLP digital solutions are transforming customer service interactions by improving customer experience, brand reputation and loyalty, and generation of revenue streams. In fact, digital market moguls project that by 2020, more than 85% of all customer support communications will be conducted without engaging any customer service representatives.
The common interactions between customers and companies point towards the reasons for customer dissatisfaction, and the interaction itself can be the cause of such discontent. However, NLP has changed the way users interact with businesses in order to create positive and upbuilding end-to-end experiences for customers. To keep a finger on the pulse of consumers’ intent, companies deploy chatbots and automated online assistants to provide immediate responses to simple needs and decrease the load for customer service representatives. Speech recognition is one of the biggest tasks that NLP takes care of. Speech recognition, which converts spoken language into text, has allowed major players and new startups to deploy this technology in commercial systems like Siri, Amazon Alexa, and Skype’s translator.
Since the 1980s, companies have tried to create software to find patterns in their own data in order to make better decisions and optimize efficiency. These changes in implementation have given rise to open course intelligence, public data that startups have adopted to look beyond their databases. While some external data is structured and ready for analysis, such as census data and stock prices, much of its value remains tapped in unstructured, human-generated text such as news, blog posts, forums, or company websites. These sources contain a plethora of precious information about how competitors, customers, and the market size as a whole are evolving.
One example of how this type of data is used is through reputation monitoring. Most customers check reviews online before buying a product. And for smaller, emerging startups that are making their presence in the market, reviews about their product are extremely important. In fact, 92% of customers read online reviews and 86% of people won’t buy a product with fewer than 3 out of 5 stars. And as consumers have resorted to voicing complaints on social media platforms such as Twitter and Facebook, reputation monitoring and management has become a top priority for businesses. Through machine learning, companies can now scan the entire web for mentions of either their brand or their products and consequently, recognize and addresses cases when they should take action.
Through simple NLP implementations, companies can conduct sentiment analysis which determines the attitude, emotional state, judgement, or intent of the user. This is done either by assigning polarity to the text (positive, neutral, or negative) or identifying the underlying mood (happy, sad, calm, etc). For sentences that include multiple attitudes, the software is able to split text into clauses, and polarity and mood are assessed for each part. For example, the sentence “The speaker function works amazing, but the app has awful glitches!” would detect the polarity for the two words “amazing” and “awful” and the content with “speaker” and “app.” NLP is transforming the way that startups are able to obtain information about how customers view their brand and products.
NLP supports daily interactions with Artificial Intelligence software, using its ability to process and interpret spoken and written messages. Most of NLP today is based on machine learning, thereby simulating what a human would in similar circumstances. With the improvements made to the venture capital firm process and made to the customer retention rates, NLP is defining venture capital culture.